A shape classifier based on Hopfield-Amari network
The representation and recognition of a planar shape based on contour information is an important issue in computer vision. We propose a method for extracting the main features of a contour using the curve bend function (CBF), which can be used to characterize the contour completely. A Hopfield-Amar...
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| container_end_page | 593 vol.1 |
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| creator | Fu, A.M.N. Hong Yan |
| description | The representation and recognition of a planar shape based on contour information is an important issue in computer vision. We propose a method for extracting the main features of a contour using the curve bend function (CBF), which can be used to characterize the contour completely. A Hopfield-Amari network is built based on the CBF description to perform classification of planar shapes. The experimental results demonstrate that the proposed system is powerful and reliable for solving shape recognition problems. |
| doi_str_mv | 10.1109/ICNN.1996.548961 |
| format | Conference Proceeding |
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We propose a method for extracting the main features of a contour using the curve bend function (CBF), which can be used to characterize the contour completely. A Hopfield-Amari network is built based on the CBF description to perform classification of planar shapes. The experimental results demonstrate that the proposed system is powerful and reliable for solving shape recognition problems.</description><identifier>ISBN: 0780332105</identifier><identifier>ISBN: 9780780332102</identifier><identifier>DOI: 10.1109/ICNN.1996.548961</identifier><language>eng</language><publisher>IEEE</publisher><subject>Associative memory ; Computer vision ; Counting circuits ; Data mining ; Feature extraction ; Hopfield neural networks ; Pattern analysis ; Pattern recognition ; Power system reliability ; Shape</subject><ispartof>Proceedings of International Conference on Neural Networks (ICNN'96), 1996, Vol.1, p.588-593 vol.1</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/548961$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/548961$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Fu, A.M.N.</creatorcontrib><creatorcontrib>Hong Yan</creatorcontrib><title>A shape classifier based on Hopfield-Amari network</title><title>Proceedings of International Conference on Neural Networks (ICNN'96)</title><addtitle>ICNN</addtitle><description>The representation and recognition of a planar shape based on contour information is an important issue in computer vision. We propose a method for extracting the main features of a contour using the curve bend function (CBF), which can be used to characterize the contour completely. A Hopfield-Amari network is built based on the CBF description to perform classification of planar shapes. The experimental results demonstrate that the proposed system is powerful and reliable for solving shape recognition problems.</description><subject>Associative memory</subject><subject>Computer vision</subject><subject>Counting circuits</subject><subject>Data mining</subject><subject>Feature extraction</subject><subject>Hopfield neural networks</subject><subject>Pattern analysis</subject><subject>Pattern recognition</subject><subject>Power system reliability</subject><subject>Shape</subject><isbn>0780332105</isbn><isbn>9780780332102</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1996</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpjYJAwNNAzNDSw1Pd09vPTM7S0NNMzNbGwNDNkZuAyMLcwMDY2MjQw5WDgLS7OMgACE1NTI3NzTgYjR4XijMSCVIXknMTi4sy0zNQihaTE4tQUhfw8BY_8AqBAToquY25iUaZCXmpJeX5RNg8Da1piTnEqL5TmZpBycw1x9tDNTE1NjS8oygQqroyH2G6MVxIApJIylA</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Fu, A.M.N.</creator><creator>Hong Yan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1996</creationdate><title>A shape classifier based on Hopfield-Amari network</title><author>Fu, A.M.N. ; Hong Yan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_5489613</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Associative memory</topic><topic>Computer vision</topic><topic>Counting circuits</topic><topic>Data mining</topic><topic>Feature extraction</topic><topic>Hopfield neural networks</topic><topic>Pattern analysis</topic><topic>Pattern recognition</topic><topic>Power system reliability</topic><topic>Shape</topic><toplevel>online_resources</toplevel><creatorcontrib>Fu, A.M.N.</creatorcontrib><creatorcontrib>Hong Yan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fu, A.M.N.</au><au>Hong Yan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A shape classifier based on Hopfield-Amari network</atitle><btitle>Proceedings of International Conference on Neural Networks (ICNN'96)</btitle><stitle>ICNN</stitle><date>1996</date><risdate>1996</risdate><volume>1</volume><spage>588</spage><epage>593 vol.1</epage><pages>588-593 vol.1</pages><isbn>0780332105</isbn><isbn>9780780332102</isbn><abstract>The representation and recognition of a planar shape based on contour information is an important issue in computer vision. We propose a method for extracting the main features of a contour using the curve bend function (CBF), which can be used to characterize the contour completely. A Hopfield-Amari network is built based on the CBF description to perform classification of planar shapes. The experimental results demonstrate that the proposed system is powerful and reliable for solving shape recognition problems.</abstract><pub>IEEE</pub><doi>10.1109/ICNN.1996.548961</doi></addata></record> |
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| identifier | ISBN: 0780332105 |
| ispartof | Proceedings of International Conference on Neural Networks (ICNN'96), 1996, Vol.1, p.588-593 vol.1 |
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| language | eng |
| recordid | cdi_ieee_primary_548961 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Associative memory Computer vision Counting circuits Data mining Feature extraction Hopfield neural networks Pattern analysis Pattern recognition Power system reliability Shape |
| title | A shape classifier based on Hopfield-Amari network |
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